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Managing deepfakes with artificial intelligence: Introducing the business privacy calculus

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  • Vecchietti, Giuseppe
  • Liyanaarachchi, Gajendra
  • Viglia, Giampaolo

Abstract

This paper explores the profound implications of artificial intelligence-driven deepfake technology. We introduce a novel business privacy calculus model by delving into the impact of deepfakes through a qualitative explanatory study involving twenty-seven bank managers from three global banks across nine countries. Building on psychological reactance and privacy calculus theories, the evidence shows how data integrity can mitigate deepfake threats, manage business risks, and ensure operational continuity. We propose an AI system architecture that operationalizes responsible AI practices aligned with the business privacy calculus framework. The study contributes to understanding deepfake threats and facilitates the development of a privacy-centric framework for AI governance to safeguard businesses, consumers, and all stakeholders more widely.

Suggested Citation

  • Vecchietti, Giuseppe & Liyanaarachchi, Gajendra & Viglia, Giampaolo, 2025. "Managing deepfakes with artificial intelligence: Introducing the business privacy calculus," Journal of Business Research, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:jbrese:v:186:y:2025:i:c:s0148296324005149
    DOI: 10.1016/j.jbusres.2024.115010
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    2. Mohammed A. Mahdi & Muhammad Asad Arshed & Amgad Muneer, 2025. "One Model for Many Fakes: Detecting GAN and Diffusion-Generated Forgeries in Faces, Invoices, and Medical Heterogeneous Data," Mathematics, MDPI, vol. 13(19), pages 1-22, September.
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